The field of the disclosure relates generally to filtering of spatial signal data, and, more specifically, to systems and methods for spatial filtering using data generated by wide area surveillance sensors and having widely different error magnitudes.
In known spatial data filtering systems and methods, reception and classification of signals is challenging where spatial data (e.g., pulse descriptor words (PDWs) in radar sensing applications) having different numbers of dimensions and widely different error magnitudes are obtained from one or more wide area sensors. In such known spatial filtering systems and methods, separation of signal from noise and interference is also problematic where the number of signals of interest is large and spatial content is a priority for classification purposes. In such known systems and methods, fusing multiple sensors having varying degrees of spatial error (e.g., ranging from very sparse to very fine spatial resolution) together for processing is inefficient absent highly complex, expensive, and memory-intensive computing architectures. The problem is compounded when known spatial filtering systems and methods require cancellation of noise and interference in order to spatially match information between sampling frames. Also, in at least some known spatial data filtering systems and methods, including those deployed in aerial surveillance operations where size, weight, and power requirements are important design considerations, improving detection range, processing and classification performance, and reducing power consumption requires increasing computation resources. Computing resources necessary for such enhancements exceed size and weight limitations for aerial surveillance platforms in at least some known spatial data filtering systems and methods, making it problematic to achieve the aforementioned improvements.
At least some known spatial data filtering systems and methods employ pre-conditioning steps such as denoising and blind source separation prior to spatial filtering, distinct methodologies and systems to process data sets with widely varying error magnitudes leads to various inefficiencies, including in accurately matching spatial data to grids of varying sparseness. Further, at least some known spatial data filtering systems and methods are unable, absent highly sophisticated, complex, and expensive post-processing architectures, to statistically join together over time spatial data-containing vectors derived from wide area sensors and having different numbers of dimensions and widely varying error magnitudes. Finally, in this context, at least some known spatial data filtering systems and methods have difficulty discerning between stationary and moving signal emitters with an acceptable error using spatial data obtained from wide area sensors.